Incidence Of Cancer In Adults Living In The Vicinity Of Nuclear Power Plants In France
Bibliographic record
Abstract
Introduction Recent results of epidemiological studies renewed the debate on health effects of environmental exposure to nuclear power plants (NPPs) emissions. Hundreds of thousands of people are living around such facilities in France. The aim of this work is to study the incidence of cancer in adults living around NPPs in France and to assess whether this approach could be extended to other French nuclear facilities. Methods We conducted an ecological study focusing on population aged 15 and over living within 20 km of 7 NPPs in France. Incidence rates were estimated for different cancers associated to chemical and radioactive releases from NPPs: liver, brain, leukaemia, female breast, ovary, bladder and thyroid cancers. Cases have been obtained from the French cancer registries at the municipality level between 1995 and 2012. This study used distance to NPPs as a proxy of exposure. A geographic correlation study, based on generalized additive models and Bayesian hierarchical models, was used to estimate the association between risk of cancer and proximity to NPPs accounting for potential confounding factors (deprivation, smoking, population density, residential mobility, roads density, presence of other industrial sites, and levels of benzene in the air). Results This study will examine cancer types by 5-years age groups, sex and calendar year. Analysis and interpretation of results are ongoing. Discussion Given the design of our study, the questions regarding cancer's incidence around NPPs will be answered. Nevertheless no causal conclusion could be drawn on this basis. This pilot study will highlight technical challenges related to health surveillance around NPPs. Data pooling with other international similar studies could enhance the efficiency of such scheme.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".